code: def encode(self, column_data):
      vec = []
      for text in column_data:
          encoded = self._tokenizer.encode(
              text[: self._max_len], add_special_tokens=True
          )
          encoded = torch.tensor(
              encoded + [self._pad_id] * (self._max_len - len(encoded))
          )
          vec.append(encoded)
      return torch.stack(vec)
  
file_path: lightwood/lightwood/encoder/text/vocab.py
imports: import os
  import torch
  from transformers import DistilBertTokenizer
  from lightwood.encoder.base import BaseEncoder
label_desc: Use image node_id 3 for calling the VocabularyEncoder obj's underlying member method code with example usage: obj.encode(column_data) and returns: torch